Voice-enabled human-machine conversational systems, such as voice interaction with entertainment systems or personal devices, depend on accurate recognition of a user's speech. For example, effective voice-search applications must accurately recognize user-submitted queries so that information returned to the user correlates with the user's intent in submitting the query. The accuracy of such systems can be improved by personalizing the language models or spoken language understanding (SLU) models used by such systems to the specific user, or groups of similar users, instead of an overall user population.
Existing approaches for personalizing language and SLU models rely primarily on certain types of past utterances by the user and personal usage patterns from user logs. These approaches assume lexical similarity to future utterances, such as future user queries; i.e., the user will ask the same questions as before. But these approaches are ineffective for future utterances that are semantically or categorically similar but contain different content. In particular, they do not provide a solution for expanding on already observed word sequence patterns in order to predict unseen user queries.